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How to create missing value for repeated measurement data?
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Closed 4 years ago.
I need to count the number of rows first after a group_by function and add up new row(s) to 6 row if the row number < 6.
My df has three variables (v1,v2,v3): v1 = group name, v2 = row number (i.e., 1,2,3,4,5,6). In the new row(s), I want to repeat the v1 value, v2 continue the couting of row number, v3 = NA
sample df
v1 v2 v3
1 1 79
1 2 32
1 3 53
1 4 33
1 5 76
1 6 11
2 1 32
2 2 42
2 3 44
2 4 12
3 1 22
3 2 12
3 3 12
3 4 67
3 5 32
expected output
v1 v2 v3
1 1 79
1 2 32
1 3 53
1 4 33
1 5 76
1 6 11
2 1 32
2 2 42
2 3 44
2 4 12
2 5 NA #insert
2 6 NA #insert
3 1 22
3 2 12
3 3 12
3 4 67
3 5 32
3 6 NA #insert
I tried to count the row number first by dplyr, but I don't know if I can or how can I add this if else condition by using the pip. Or is there other easier function?
My code
df %>%
group_by(v1) %>%
dplyr::summarise(N=n()) %>%
if (N < 6) {
# sth like that?
}
Thanks!
We can use complete
library(tidyverse)
complete(df1, v1, v2)
# A tibble: 18 x 3
# v1 v2 v3
# <int> <int> <int>
# 1 1 1 79
# 2 1 2 32
# 3 1 3 53
# 4 1 4 33
# 5 1 5 76
# 6 1 6 11
# 7 2 1 32
# 8 2 2 42
# 9 2 3 44
#10 2 4 12
#11 2 5 NA
#12 2 6 NA
#13 3 1 22
#14 3 2 12
#15 3 3 12
#16 3 4 67
#17 3 5 32
#18 3 6 NA
Here is a way to do it using merge.
df <- read.table(text =
"v1 v2 v3
1 1 79
1 2 32
1 3 53
1 4 33
1 5 76
1 6 11
2 1 32
2 2 42
2 3 44
2 4 12
3 1 22
3 2 12
3 3 12
3 4 67
3 5 32", header = T)
toMerge <- data.frame(v1 = rep(1:3, each = 6), v2 = rep(1:6, times = 3))
m <- merge(toMerge, df, by = c("v1", "v2"), all.x = T)
m
v1 v2 v3
1 1 1 79
2 1 2 32
3 1 3 53
4 1 4 33
5 1 5 76
6 1 6 11
7 2 1 32
8 2 2 42
9 2 3 44
10 2 4 12
11 2 5 NA
12 2 6 NA
13 3 1 22
14 3 2 12
15 3 3 12
16 3 4 67
17 3 5 32
18 3 6 NA
Related
I have 4 datasets from 4 rounds of a survey, with the first round containing 5 variables and the next ones containing only 3. This is because the ID (same sample) and the other two variables (v1 and v2) are fixed over time.
df1 <- data.frame(id = c(1:5), round=1, v1 = c(6:10), v2 = c(11:15), v3=c(16:20))
df2 <- data.frame(id = c(1:5), round=2, v3=c(26:30))
df3 <- data.frame(id = c(1:5), round=3, v3=c(36:40))
df4 <- data.frame(id = c(1:5), round=4, v3=c(46:50))
** rbind
list(df1, df2, df3, df4) %>%
bind_rows(.id = 'grp') %>%
group_by(id)
Now when I rbind them, I end up with missing rows for the two fixed variables for rounds 1 to 3:
grp id round v1 v2 v3
<chr> <int> <dbl> <int> <int> <int>
1 1 1 1 6 11 16
2 1 2 1 7 12 17
3 1 3 1 8 13 18
4 1 4 1 9 14 19
5 1 5 1 10 15 20
6 2 1 2 NA NA 26
7 2 2 2 NA NA 27
8 2 3 2 NA NA 28
9 2 4 2 NA NA 29
10 2 5 2 NA NA 30
11 3 1 3 NA NA 36
12 3 2 3 NA NA 37
13 3 3 3 NA NA 38
14 3 4 3 NA NA 39
15 3 5 3 NA NA 40
16 4 1 4 NA NA 46
17 4 2 4 NA NA 47
18 4 3 4 NA NA 48
19 4 4 4 NA NA 49
20 4 5 4 NA NA 50
but I need v1 and v2 to be filled for the next rounds as well by matching the respective ID.
Please let me know if there is any way to do this in R (or in Python).
Thank you.
list(df1, df2, df3, df4) %>%
bind_rows(.id = 'grp') %>%
group_by(id) %>%
fill(v1:v3) # from tidyr
#fill(4:6) # alternative syntax: columns 4-6
#fill(-c(1:3)) # alternative syntax: everything except columns 1:3
#fill(everything()) # alternative syntax: fill NAs in all columns
grp id round v1 v2 v3
<chr> <int> <dbl> <int> <int> <int>
1 1 1 1 6 11 16
2 1 2 1 7 12 17
3 1 3 1 8 13 18
4 1 4 1 9 14 19
5 1 5 1 10 15 20
6 2 1 2 6 11 26
7 2 2 2 7 12 27
8 2 3 2 8 13 28
9 2 4 2 9 14 29
10 2 5 2 10 15 30
11 3 1 3 6 11 36
12 3 2 3 7 12 37
13 3 3 3 8 13 38
14 3 4 3 9 14 39
15 3 5 3 10 15 40
16 4 1 4 6 11 46
17 4 2 4 7 12 47
18 4 3 4 8 13 48
19 4 4 4 9 14 49
20 4 5 4 10 15 50
I have a dataframe with 2 columns: time and day. there are 3 days and for each day, time runs from 1 to 12. I want to add new rows for each day with times: -2, 1 and 0. How do I do this?
I have tried using add_row and specifying the row number to add to, but this changes each time a new row is added making the process tedious. Thanks in advance
picture of the dataframe
We could use add_row
then slice the desired sequence
and bind all to a dataframe:
library(tibble)
library(dplyr)
df1 <- df %>%
add_row(time = -2:0, Day = c(1,1,1), .before = 1) %>%
slice(1:15)
df2 <- bind_rows(df1, df1, df1) %>%
mutate(Day = rep(row_number(), each=15, length.out = n()))
Output:
# A tibble: 45 x 2
time Day
<dbl> <int>
1 -2 1
2 -1 1
3 0 1
4 1 1
5 2 1
6 3 1
7 4 1
8 5 1
9 6 1
10 7 1
11 8 1
12 9 1
13 10 1
14 11 1
15 12 1
16 -2 2
17 -1 2
18 0 2
19 1 2
20 2 2
21 3 2
22 4 2
23 5 2
24 6 2
25 7 2
26 8 2
27 9 2
28 10 2
29 11 2
30 12 2
31 -2 3
32 -1 3
33 0 3
34 1 3
35 2 3
36 3 3
37 4 3
38 5 3
39 6 3
40 7 3
41 8 3
42 9 3
43 10 3
44 11 3
45 12 3
Here's a fast way to create the desired dataframe from scratch using expand.grid(), rather than adding individual rows:
df <- expand.grid(-2:12,1:3)
colnames(df) <- c("time","day")
Results:
df
time day
1 -2 1
2 -1 1
3 0 1
4 1 1
5 2 1
6 3 1
7 4 1
8 5 1
9 6 1
10 7 1
11 8 1
12 9 1
13 10 1
14 11 1
15 12 1
16 -2 2
17 -1 2
18 0 2
19 1 2
20 2 2
21 3 2
22 4 2
23 5 2
24 6 2
25 7 2
26 8 2
27 9 2
28 10 2
29 11 2
30 12 2
31 -2 3
32 -1 3
33 0 3
34 1 3
35 2 3
36 3 3
37 4 3
38 5 3
39 6 3
40 7 3
41 8 3
42 9 3
43 10 3
44 11 3
45 12 3
You can use tidyr::crossing
library(dplyr)
library(tidyr)
add_values <- c(-2, 1, 0)
crossing(time = add_values, Day = unique(day$Day)) %>%
bind_rows(day) %>%
arrange(Day, time)
# A tibble: 45 x 2
# time Day
# <dbl> <int>
# 1 -2 1
# 2 0 1
# 3 1 1
# 4 1 1
# 5 2 1
# 6 3 1
# 7 4 1
# 8 5 1
# 9 6 1
#10 7 1
# … with 35 more rows
If you meant -2, -1 and 0 you can also use complete.
tidyr::complete(day, Day, time = -2:0)
A dataframe DD has some missing rows. Based on the values in 'ID_raw' column I have duplicated the rows in order to replace the missing rows. Now I have to number the rows in such way that the first value in each group (column 'File') equals the value in the same row in the column 'ID_raw'. This will be a key in joining the dataframe with another one. Below a dummy example of the DD dataframe:
DD<-data.frame(ID_raw=c(1,5,7,8,5,7,9,13,3,6),Val=c(1,2,8,15,54,23,88,77,32,2),File=c("A","A","A","A","B","B","B","B","C","C"))
ID_raw Val File
1 1 1 A
2 5 2 A
3 7 8 A
4 8 15 A
5 5 54 B
6 7 23 B
7 9 88 B
8 13 77 B
9 3 32 C
10 6 2 C
So far I've successfully duplicated the rows, however, I have a problem in numbering the rows in such way, that they start from the same value as the value in ID_raw column for each group ('File').
DD$ID_diff <- 0
DD$ID_diff[1:nrow(DD)-1] <- as.integer(diff(DD$ID_raw, 1)) #values which tell how many times a row has to be duplicated
DD$ID_diff <- sapply(DD$ID_diff, function(x) ifelse(x<0, 0, x)) #replacement the values <0 (for the first rows in each 'File' group)
DD <- DD[rep(seq(nrow(DD)), DD$ID_diff), 1:ncol(DD)] #rows duplication
Based on the code above I receive this output:
ID_raw Val File ID_diff
1 1 1 A 4
1.1 1 1 A 4
1.2 1 1 A 4
1.3 1 1 A 4
2 5 2 A 2
2.1 5 2 A 2
3 7 8 A 1
5 5 54 B 2
5.1 5 54 B 2
6 7 23 B 2
6.1 7 23 B 2
7 9 88 B 4
7.1 9 88 B 4
7.2 9 88 B 4
7.3 9 88 B 4
9 3 32 C 3
9.1 3 32 C 3
9.2 3 32 C 3
I would like to receive this:
ID_raw Val File ID_diff ID_new
1 1 1 A 4 1
1.1 1 1 A 4 2
1.2 1 1 A 4 3
1.3 1 1 A 4 4
2 5 2 A 2 5
2.1 5 2 A 2 6
3 7 8 A 1 7
5 5 54 B 2 5
5.1 5 54 B 2 6
6 7 23 B 2 7
6.1 7 23 B 2 8
7 9 88 B 4 9
7.1 9 88 B 4 10
7.2 9 88 B 4 11
7.3 9 88 B 4 12
9 3 32 C 3 3
9.1 3 32 C 3 4
9.2 3 32 C 3 5
This is one option using dplyr based on the output of your code:
df %>%
group_by(File) %>%
mutate(ID_new = seq(1, n()) + first(ID_raw) - 1)
# A tibble: 18 x 5
# Groups: File [3]
ID_raw Val File ID_diff ID_new
<int> <int> <fct> <int> <dbl>
1 1 1 A 4 1
2 1 1 A 4 2
3 1 1 A 4 3
4 1 1 A 4 4
5 5 2 A 2 5
6 5 2 A 2 6
7 7 8 A 1 7
8 5 54 B 2 5
9 5 54 B 2 6
10 7 23 B 2 7
11 7 23 B 2 8
12 9 88 B 4 9
13 9 88 B 4 10
14 9 88 B 4 11
15 9 88 B 4 12
16 3 32 C 3 3
17 3 32 C 3 4
18 3 32 C 3 5
We can do this in the chain from the beginning itself i.e. instead of creating the 'ID_diff' and using sapply, directly use diff on the 'ID_raw', then uncount, grouped by 'File', create the sequence column
library(tidyverse)
DD %>%
mutate(ID_diff = pmax(c(diff(ID_raw), 0), 0)) %>%
uncount(ID_diff, .remove = FALSE) %>%
group_by(File) %>%
mutate(ID_new = seq(first(ID_raw), length.out = n(), by = 1))
# A tibble: 18 x 5
# Groups: File [3]
# ID_raw Val File ID_diff ID_new
# <dbl> <dbl> <fct> <dbl> <dbl>
# 1 1 1 A 4 1
# 2 1 1 A 4 2
# 3 1 1 A 4 3
# 4 1 1 A 4 4
# 5 5 2 A 2 5
# 6 5 2 A 2 6
# 7 7 8 A 1 7
# 8 5 54 B 2 5
# 9 5 54 B 2 6
#10 7 23 B 2 7
#11 7 23 B 2 8
#12 9 88 B 4 9
#13 9 88 B 4 10
#14 9 88 B 4 11
#15 9 88 B 4 12
#16 3 32 C 3 3
#17 3 32 C 3 4
#18 3 32 C 3 5
I have some data where each id is measured by different types which can be have different values type_val. The measured value is val. A small dummy data is like this:
df <- data.frame(id=rep(letters[1:2],6),
type=c(rep('t1',6), rep('t2',6)),
type_val=rep(c(1,1,2,2,3,3),2),
val=1:12)
Then df is:
id type type_val val
1 a t1 1 1
2 b t1 1 2
3 a t1 2 3
4 b t1 2 4
5 a t1 3 5
6 b t1 3 6
7 a t2 1 7
8 b t2 1 8
9 a t2 2 9
10 b t2 2 10
11 a t2 3 11
12 b t2 3 12
I need to spread/cast data so that all combinations of type and type_val for each id are row-wise. I think this must be a job for pkgs reshape2 or tidyr but I have completely failed to generate anything other than errors.
The outcome data structure - somewhat redundant - would be something like this (hope I got it right!) where pairs of type (as given by combinations of the type_val) are columns type_t1 and type_t2 , and their associated values (val in df) are val_t1 and val_t2 - columns names are of cause arbitrary :
id type_t1 type_t2 val_t1 val_t2
1 a 1 1 1 7
2 a 1 2 1 9
3 a 1 3 1 11
4 a 2 1 3 7
5 a 2 2 3 9
6 a 2 3 3 11
7 a 3 1 5 7
8 a 3 2 5 9
9 a 3 3 5 11
10 b 1 1 2 8
11 b 1 2 2 10
12 b 1 3 2 12
13 b 2 1 4 8
14 b 2 2 4 10
15 b 2 3 4 12
16 b 3 1 6 8
17 b 3 2 6 10
18 b 3 3 6 12
UPDATE
Note that (#Sotos)
> spread(df, type, val)
id type_val t1 t2
1 a 1 1 7
2 a 2 3 9
3 a 3 5 11
4 b 1 2 8
5 b 2 4 10
6 b 3 6 12
is not the desired output - it fails to deliver the wide format defined by combinations of type and type_val in df.
how about this:
df1=df[df$type=="t1",]
df2=df[df$type=="t2",]
DF=merge(df1,df2,by="id")
DF=DF[,-c(2,5)]
colnames(DF)<-c("id", "type_t1", "val_t1","type_t2", "val_t2")
Here is something more generic that will work with an arbitrary number of unique type:
library(dplyr)
# This function takes a list of dataframes (.data) and merges them by ID
reduce_merge <- function(.data, ID) {
return(Reduce(function(x, y) merge(x, y, by = ID), .data))
}
# This function renames the cols columns in .data by appending _identifier
batch_rename <- function(.data, cols, identifier, sep = '_') {
return(plyr::rename(.data, sapply(cols, function(x){
x = paste(x, .data[1, identifier], sep = sep)
})))
}
# This function creates a list of subsetted dataframes
# (subsetted by values of key),
# uses batch_rename() to give each dataframe more informative column names,
# merges them together, and returns the columns you'd like in a sensible order
multi_spread <- function(.data, grp, key, vals) {
.data %>%
plyr::dlply(key, subset) %>%
lapply(batch_rename, vals, key) %>%
reduce_merge(grp) %>%
select(-starts_with(paste0(key, '.'))) %>%
select(id, sort(setdiff(colnames(.), c(grp, key, vals))))
}
# Your example
df <- data.frame(id=rep(letters[1:2],6),
type=c(rep('t1',6), rep('t2',6)),
type_val=rep(c(1,1,2,2,3,3),2),
val=1:12)
df %>% multi_spread('id', 'type', c('type_val', 'val'))
id type_val_t1 type_val_t2 val_t1 val_t2
1 a 1 1 1 7
2 a 1 2 1 9
3 a 1 3 1 11
4 a 2 1 3 7
5 a 2 2 3 9
6 a 2 3 3 11
7 a 3 1 5 7
8 a 3 2 5 9
9 a 3 3 5 11
10 b 1 1 2 8
11 b 1 2 2 10
12 b 1 3 2 12
13 b 2 1 4 8
14 b 2 2 4 10
15 b 2 3 4 12
16 b 3 1 6 8
17 b 3 2 6 10
18 b 3 3 6 12
# An example with three unique values of 'type'
df <- data.frame(id = rep(letters[1:2], 9),
type = c(rep('t1', 6), rep('t2', 6), rep('t3', 6)),
type_val = rep(c(1, 1, 2, 2, 3, 3), 3),
val = 1:18)
df %>% multi_spread('id', 'type', c('type_val', 'val'))
id type_val_t1 type_val_t2 type_val_t3 val_t1 val_t2 val_t3
1 a 1 1 1 1 7 13
2 a 1 1 2 1 7 15
3 a 1 1 3 1 7 17
4 a 1 2 1 1 9 13
5 a 1 2 2 1 9 15
6 a 1 2 3 1 9 17
7 a 1 3 1 1 11 13
8 a 1 3 2 1 11 15
9 a 1 3 3 1 11 17
10 a 2 1 1 3 7 13
11 a 2 1 2 3 7 15
12 a 2 1 3 3 7 17
13 a 2 2 1 3 9 13
14 a 2 2 2 3 9 15
15 a 2 2 3 3 9 17
16 a 2 3 1 3 11 13
17 a 2 3 2 3 11 15
18 a 2 3 3 3 11 17
19 a 3 1 1 5 7 13
20 a 3 1 2 5 7 15
21 a 3 1 3 5 7 17
22 a 3 2 1 5 9 13
23 a 3 2 2 5 9 15
24 a 3 2 3 5 9 17
25 a 3 3 1 5 11 13
26 a 3 3 2 5 11 15
27 a 3 3 3 5 11 17
28 b 1 1 1 2 8 14
29 b 1 1 2 2 8 16
30 b 1 1 3 2 8 18
31 b 1 2 1 2 10 14
32 b 1 2 2 2 10 16
33 b 1 2 3 2 10 18
34 b 1 3 1 2 12 14
35 b 1 3 2 2 12 16
36 b 1 3 3 2 12 18
37 b 2 1 1 4 8 14
38 b 2 1 2 4 8 16
39 b 2 1 3 4 8 18
40 b 2 2 1 4 10 14
41 b 2 2 2 4 10 16
42 b 2 2 3 4 10 18
43 b 2 3 1 4 12 14
44 b 2 3 2 4 12 16
45 b 2 3 3 4 12 18
46 b 3 1 1 6 8 14
47 b 3 1 2 6 8 16
48 b 3 1 3 6 8 18
49 b 3 2 1 6 10 14
50 b 3 2 2 6 10 16
51 b 3 2 3 6 10 18
52 b 3 3 1 6 12 14
53 b 3 3 2 6 12 16
54 b 3 3 3 6 12 18
Here an example of my data.frame:
df = read.table(text='colA colB colC
10 11 7
10 34 7
10 89 7
10 21 7
2 23 5
2 21 5
2 56 5
22 14 3
22 19 3
22 90 3
11 19 2
11 45 2
1 45 0
1 23 0
9 8 0
9 11 0
9 21 0', header = TRUE)
I need to group the rows by colA and colC and add a new column which states the sum of unique values based on colB.
In steps here what I need to do for this specific data.frame:
group rows with colA = 10 and 9, colA = 2 and 1, colA = 22 and colA = 11;
find the unique values of colB per each group;
add the unique values in a new col (newcolD).
Note that colC states the total number of observations for colA = 10 and 9, colA = 2 and 1, colA = 22 and colA = 11.
The data.frame needs to remain ordered decreasingly by colC.
My expected output is:
colA colB colC newcolD
10 11 7 5
10 34 7 5
10 89 7 5
10 21 7 5
9 8 0 5
9 11 0 5
9 21 0 5
2 23 5 4
2 21 5 4
2 56 5 4
1 45 0 4
1 23 0 4
22 14 3 3
22 19 3 3
22 90 3 3
11 19 2 2
11 45 2 2
To note that in df the colB duplicated values are: 11 and 21 for group 10 and 9, and 23 for group 2 and 1.
You can do that with dplyr. The trick is to create a new grouping column which groups consecutive values in colA. This is done with cumsum(c(1, diff(colA) < -1) in the example below.
df1 = read.table(text='colA colB colC
10 11 7
10 34 7
10 89 7
10 21 7
2 23 5
2 21 5
2 56 5
22 14 3
22 19 3
22 90 3
1 45 0
1 23 0
9 8 0
9 11 0
9 21 0', header = TRUE,stringsAsFactors=FALSE)
library(dplyr)
df1 %>%
arrange(desc(colA)) %>%
group_by(group_sequential = cumsum(c(1, diff(colA) < -1))) %>%
mutate(newcolD=n_distinct(colB))
colA colB colC group_sequential newcolD
<int> <int> <int> <dbl> <int>
1 22 14 3 1 3
2 22 19 3 1 3
3 22 90 3 1 3
4 10 11 7 2 5
5 10 34 7 2 5
6 10 89 7 2 5
7 10 21 7 2 5
8 9 8 0 2 5
9 9 11 0 2 5
10 9 21 0 2 5
11 2 23 5 3 4
12 2 21 5 3 4
13 2 56 5 3 4
14 1 45 0 3 4
15 1 23 0 3 4
EDIT FOR NEW DATA
With the data you added, we need to create a custom grouping. I use case_when in the example below. This matches the order you show in the desired output column. In the text, you wrote that you wanted the table to be sorted by colC. To do so, change the last line to arrange(desc(colC))
df1 = read.table(text='colA colB colC
10 11 7
10 34 7
10 89 7
10 21 7
2 23 5
2 21 5
2 56 5
22 14 3
22 19 3
22 90 3
11 19 2
11 45 2
1 45 0
1 23 0
9 8 0
9 11 0
9 21 0', header = TRUE,stringsAsFactors=FALSE)
library(dplyr)
df1 %>%
group_by(group_sequential = case_when(.$colA==10|.$colA==9~1,
.$colA==2|.$colA==1~2,
.$colA==22~3,
.$colA==11~4)) %>%
mutate(newcolD=n_distinct(colB)) %>%
arrange(desc(newcolD))
colA colB colC group_sequential newcolD
<int> <int> <int> <dbl> <int>
1 10 11 7 1 5
2 10 34 7 1 5
3 10 89 7 1 5
4 10 21 7 1 5
5 9 8 0 1 5
6 9 11 0 1 5
7 9 21 0 1 5
8 2 23 5 2 4
9 2 21 5 2 4
10 2 56 5 2 4
11 1 45 0 2 4
12 1 23 0 2 4
13 22 14 3 3 3
14 22 19 3 3 3
15 22 90 3 3 3
16 11 19 2 4 2
17 11 45 2 4 2
You're really not making it easy for us, reposting slight variations of the same question instead of updating the old one and presenting conditions that are vague and inconsistent with what the desired output implies. Anyhow, here is my attempt. This is more an answer to the second question you posted, as that was a bit more general in form.
It's a bit messy, it's pretty much a direct translation of your conditions into a for loop with some if statements. I chose to focus on your written conditions rather than the expected output as that was the easier one to understand. If you want a better answer, please consider cleaning up you question(s) considerably.
df1 <- read.table(text="
colA colB colC
10 11 7
10 34 7
10 89 7
10 21 7
2 23 5
2 21 5
2 56 5
22 14 3
22 19 3
22 90 3
11 19 2
11 45 2
1 45 0
1 23 0
9 8 0
9 11 0
9 21 0", header=TRUE)
df2 <- read.table(text="
colA colB colC
10 11 7
10 34 7
10 89 7
10 21 7
2 23 5
2 21 5
2 56 5
33 24 3
33 78 3
22 14 3
22 19 3
22 90 3
11 19 2
11 45 2
1 45 0
1 23 0
9 8 0
9 11 0
9 21 0
32 11 0", header=TRUE)
df <- df1
for (i in 1:nrow(df)) {
df$colD[i] <- ifelse(df$colC[i] == 0,
0,
length(unique(df$colA[1:i])))
if (any(df$colA[i]-1 == df$colA[1:i]) & df$colC[i] != 0) {
df$colD[i] <- df$colD[which(df$colA[i]-1 == df$colA[1:i])][1]
}
}
# colA colB colC colD
# 10 11 7 1
# 10 34 7 1
# 10 89 7 1
# 10 21 7 1
# 2 23 5 2
# 2 21 5 2
# 2 56 5 2
# 22 14 3 3
# 22 19 3 3
# 22 90 3 3
# 11 19 2 1
# 11 45 2 1
# 1 45 0 0
# 1 23 0 0
# 9 8 0 0
# 9 11 0 0
# 9 21 0 0
df <- df2
for (i in 1:nrow(df)) {
df$colD[i] <- ifelse(df$colC[i] == 0,
0,
length(unique(df$colA[1:i])))
if (any(df$colA[i]-1 == df$colA[1:i]) & df$colC[i] != 0) {
df$colD[i] <- df$colD[which(df$colA[i]-1 == df$colA[1:i])][1]
}
}
df
# colA colB colC colD
# 10 11 7 1
# 10 34 7 1
# 10 89 7 1
# 10 21 7 1
# 2 23 5 2
# 2 21 5 2
# 2 56 5 2
# 33 24 3 3
# 33 78 3 3
# 22 14 3 4
# 22 19 3 4
# 22 90 3 4
# 11 19 2 1
# 11 45 2 1
# 1 45 0 0
# 1 23 0 0
# 9 8 0 0
# 9 11 0 0
# 9 21 0 0
# 32 11 0 0
To also group the rows where colC is zero, it's sufficient to adjust the conditionals like this:
for (i in 1:nrow(df)) {
df$colD[i] <- length(unique(df$colA[1:i]))
if (any(df$colA[i]-1 == df$colA[1:i])) {
df$colD[i] <- df$colD[which(df$colA[i]-1 == df$colA[1:i])][1]
}
}